{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d01e15b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "反演三条支路流量（Branch1/2/3）\n",
    "Author: your_name\n",
    "Date: 2025-07-11\n",
    "\"\"\"\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.optimize import nnls\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ca089072",
   "metadata": {},
   "outputs": [],
   "source": [
    "# -------------------------------------------------\n",
    "# 1️⃣ 读 Excel 第三张表（索引 2）\n",
    "# -------------------------------------------------\n",
    "FILE_PATH = '附件(Attachment).xlsx'\n",
    "df_raw = pd.read_excel(FILE_PATH, 2)\n",
    "df=df_raw.iloc[:,1:3]\n",
    "df.columns = ['t', 'A3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e664ace5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# --------------------------\n",
    "# 1. 支路1：5 段三次 Hermite 或 5 段线性\n",
    "#    节点：t0=5, t1=15, t2=25, t3=59\n",
    "#    参数：a0-a4（5 个幅值）+ 斜率 2 个（增长、下降）\n",
    "#    这里用简单线性拼接\n",
    "def f1_model(t, theta):\n",
    "    \"\"\"\n",
    "    theta = [a0, a1, a2, a3, a4]\n",
    "    段1: [0,5)   = 0\n",
    "    段2: [5,15)  = a0 + k1*(t-5)\n",
    "    段3: [15,25) = a1 + k2*(t-15)\n",
    "    段4: [25,59) = a2\n",
    "    简化：取 a0=0, a2=0，只留 3 个参数\n",
    "    \"\"\"\n",
    "    k1, k2, a1 = theta[0], theta[1], theta[2]\n",
    "    out = np.zeros_like(t, dtype=float)\n",
    "    mask2 = (t >= 5) & (t < 15)\n",
    "    mask3 = (t >= 15) & (t < 25)\n",
    "    mask4 = (t >= 25)\n",
    "    out[mask2] = k1 * (t[mask2] - 5)\n",
    "    out[mask3] = a1 + k2 * (t[mask3] - 15)\n",
    "    return out\n",
    "\n",
    "# --------------------------\n",
    "# 2. 支路2：三段线性\n",
    "#    参数：s_up, s_plat, s_down, h_plat\n",
    "def f2_model(t, theta):\n",
    "    s_up, s_plat, s_down, h_plat = theta\n",
    "    out = np.zeros_like(t, dtype=float)\n",
    "    # 0-35 上升\n",
    "    mask1 = t <= 35\n",
    "    out[mask1] = s_up * t[mask1]\n",
    "    # 35-47 平台\n",
    "    mask2 = (t > 35) & (t <= 47)\n",
    "    out[mask2] = h_plat\n",
    "    # 47-59 下降\n",
    "    mask3 = t > 47\n",
    "    out[mask3] = h_plat + s_down * (t[mask3] - 47)\n",
    "    return out\n",
    "\n",
    "# --------------------------\n",
    "# 3. 支路3：周期矩形\n",
    "#    参数：height\n",
    "#    已知周期 18 min，绿灯 10 min 红灯 8 min\n",
    "def f3_model(t, theta):\n",
    "    height = theta[0]\n",
    "    period = 9               # 单位：2 min -> 18 min\n",
    "    green  = 5               # 5*2=10 min\n",
    "    phase  = 3               # 第一个绿灯起点 t=3\n",
    "    cycle_pos = (t - phase) % period\n",
    "    out = np.where(cycle_pos < green, height, 0.0)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7c3ff55c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def F_all(t, theta):\n",
    "    \"\"\"\n",
    "    theta = [k1,k2,a1,  s_up,s_plat,s_down,h_plat,  h3]\n",
    "    共 8 个参数\n",
    "    \"\"\"\n",
    "    th1 = theta[:3]\n",
    "    th2 = theta[3:7]\n",
    "    th3 = theta[7:8]\n",
    "    # 延迟 1 个时间单位（2 min）\n",
    "    f1 = f1_model(t-1, th1)\n",
    "    f2 = f2_model(t-1, th2)\n",
    "    f3 = f3_model(t,   th3)\n",
    "    return f1 + f2 + f3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b052e7e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss(theta, t_obs, y_obs):\n",
    "    y_pred = F_all(t_obs, theta)\n",
    "    return np.sum((y_pred - y_obs)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8b507b63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "优化后参数： [ 3.41041919  0.         29.33918306  2.94487911 15.          0.\n",
      " 52.47818939 13.8078073 ]\n"
     ]
    }
   ],
   "source": [
    "from scipy.optimize import minimize\n",
    "\n",
    "t_obs = df['t'].values\n",
    "y_obs = df['A3'].values\n",
    "\n",
    "theta0 = [5, -5, 50,        # f1 初值\n",
    "          0.4, 15, -1.2, 15,  # f2 初值\n",
    "          10]                # f3 初值\n",
    "\n",
    "res = minimize(loss, theta0, args=(t_obs, y_obs),\n",
    "               method='L-BFGS-B', bounds=[(0,None)]*8)\n",
    "\n",
    "theta_opt = res.x\n",
    "print('优化后参数：', theta_opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8be1244a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "\n",
    "# 主路4原始观测（前面已读 df）\n",
    "t_obs = df['t'].values\n",
    "y_obs = df['A3'].values\n",
    "\n",
    "# -------------------------------------------------\n",
    "# 1. 生成 3 条支路 + 主路 4 的模型值（300 点平滑）\n",
    "# -------------------------------------------------\n",
    "t_plot = np.linspace(0, 59, 300)\n",
    "\n",
    "f1_curve = f1_model(t_plot, theta_opt[:3])\n",
    "f2_curve = f2_model(t_plot, theta_opt[3:7])\n",
    "f3_curve = f3_model(t_plot, theta_opt[7:8])\n",
    "\n",
    "# 主路 4 模型值（含 2 min 延迟）\n",
    "f1_del = np.interp(t_plot-1, t_plot, f1_curve, left=0)\n",
    "f2_del = np.interp(t_plot-1, t_plot, f2_curve, left=0)\n",
    "main_model = f1_del + f2_del + f3_curve\n",
    "\n",
    "# -------------------------------------------------\n",
    "# 2. 画图\n",
    "# -------------------------------------------------\n",
    "fig, axes = plt.subplots(4, 1, figsize=(14, 10), sharex=True)\n",
    "\n",
    "# -- 主路 4\n",
    "ax = axes[0]\n",
    "ax.scatter(t_obs, y_obs, color='black', zorder=5, label='Observed Main Road 4', s=25)\n",
    "ax.plot(t_plot, main_model, color='#0072B2', lw=2, label='Fitted Main Road 4')\n",
    "ax.axvline(15, ls='--', color='red', lw=1); ax.text(15.2, max(y_obs)*0.9, '7:30', color='red')\n",
    "ax.axvline(45, ls='--', color='red', lw=1); ax.text(45.2, max(y_obs)*0.9, '8:30', color='red')\n",
    "ax.set_title('Main Road 4 (A3) - Observed vs Fitted'); ax.legend()\n",
    "\n",
    "# -- 支路 1\n",
    "ax = axes[1]\n",
    "ax.plot(t_plot, f1_curve, color='#D55E00', lw=2)\n",
    "ax.fill_between(t_plot, 0, f1_curve, color='#D55E00', alpha=0.2)\n",
    "ax.set_ylabel('Branch 1 Flow'); ax.set_title('Branch 1 (delayed 2 min)')\n",
    "\n",
    "# -- 支路 2\n",
    "ax = axes[2]\n",
    "ax.plot(t_plot, f2_curve, color='#009E73', lw=2)\n",
    "ax.fill_between(t_plot, 0, f2_curve, color='#009E73', alpha=0.2)\n",
    "ax.set_ylabel('Branch 2 Flow'); ax.set_title('Branch 2 (delayed 2 min)')\n",
    "\n",
    "# -- 支路 3\n",
    "ax = axes[3]\n",
    "ax.step(t_plot, f3_curve, where='post', color='#CC79A7', lw=2)\n",
    "ax.fill_between(t_plot, 0, f3_curve, step='post', color='#CC79A7', alpha=0.2)\n",
    "ax.set_ylabel('Branch 3 Flow'); ax.set_xlabel('t (2-min since 07:00)')\n",
    "ax.set_title('Branch 3 (no delay, traffic light)')\n",
    "\n",
    "# 美化\n",
    "for ax in axes:\n",
    "    ax.axvline(15, ls='--', color='red', lw=1, alpha=0.6)\n",
    "    ax.axvline(45, ls='--', color='red', lw=1, alpha=0.6)\n",
    "\n",
    "plt.suptitle('Traffic Flow Decomposition', fontsize=16)\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.97])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
